Cyber-Malware Defense for Smart Grids Using Machine Learning

Muhammad Imran Ghafoor, Shah Marjan, Muhammad Shoaib Roomi, Abdul Wahid, Shumaila Hussain, Mehmood Baryalai, Shapoor Alikhail


Smart grids are frequently targeted by false data injection, also known as FDI. Foreign direct interference (FDI) cannot be detected using data detection methods that are insufficient today. FDI attacks can be detected using a variety of methods, including machine learning. The purpose of this study is to look at six different supervised learning (CNN RF-FS) hybrid strategies. In each of these techniques, CNN RF-Boosting algorithms are applied to boost and select features. An analysis of smart grid adaptability is conducted using data from these systems. There are various methods of detection when it comes to categorizing objects. In a simulation, supervised learning and hybrid approaches can help detect FDI attacks using classification systems.


Foreign direct interference, supervised learning, neural network, Data classification, Boosting algorithms

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